Realised Variance Forecasting Under Box-Cox Transformations

Abstract

The benefits associated with modeling Box-Cox transformed realised variance data are assessed. In particular, the quality of realised variance forecasts with and without this transformation applied are examined in an out-of-sample forecasting competition. Using various realised variance measures, data transformations, volatility models and assessment methods, and controlling for data mining issues, the results indicate that data transformations can be economically and statistically significant. Moreover, the quartic transformation appears to be the most effective in this regard. The conditions under which the effectiveness of using transformed data varies are identified.